scholarly journals Monitoring newt communities in urban area using eDNA metabarcoding

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12357
Author(s):  
Léo Charvoz ◽  
Laure Apothéloz-Perret-Gentil ◽  
Emanuela Reo ◽  
Jacques Thiébaud ◽  
Jan Pawlowski
Keyword(s):  
16S Rrna ◽  
Urban Area ◽  
Urban Areas ◽  
Environmental Dna ◽  
Visual Observation ◽  
Individual Species ◽  
Invasive Monitoring ◽  
Non Invasive ◽  
Italian Peninsula ◽  
Lissotriton Vulgaris

Newts are amphibians commonly present in small ponds or garden pools in urban areas. They are protected in many countries and their presence is monitored through visual observation and/or trapping. However, newts are not easy to spot as they are small, elusive and often hidden at the bottom of water bodies. In recent years, environmental DNA (eDNA) has become a popular tool for detecting newts, with a focus on individual species using qPCR assays. Here, we assess the effectiveness of eDNA metabarcoding compared to conventional visual surveys of newt diversity in 45 ponds within urban areas of Geneva canton, Switzerland. We designed newt-specific mitochondrial 16S rRNA primers, which assign the majority of amplicons to newts, and were able to detect four species known to be present in the region, including the invasive subspecies Lissotriton vulgaris meridionalis, native to the Italian peninsula, that has been introduced in the Geneva area recently. The obtained eDNA results were congruent overall with conventional surveys, confirming the morphological observations in the majority of cases (67%). In 25% of cases, a species was only detected genetically, while in 8% of cases, the observations were not supported by eDNA metabarcoding. Our study confirms the usefulness of eDNA metabarcoding as a tool for the effective and non-invasive monitoring of newt community and suggests its broader use for the survey of newt diversity in urban area at larger scales.

The Status of Livelihood Vulnerability and Its CopingCapacities of Urban Squatters in Kathmandu Metropolitan City, Nepal

Patan Pragya ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 19-32
Author(s):  
Chhabi Ram Baral
Keyword(s):  
Data Collection ◽  
Urban Area ◽  
Urban Areas ◽  
Quantitative Methods ◽  
Urban Poverty ◽  
Rural Poverty ◽  
Poor People ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
The Status

Urban poverty is one of multidimensional issue in Nepal. Increasing immigration from the outer parts of Kathmandu due to rural poverty, unemployment and weak security of the lives and the properties are core causes pushing people into urban areas. In this context how squatter urban area people sustain their livelihoods is major concern. The objectives of the study are to find out livelihood assets and capacities squatters coping with their livelihood vulnerability in adverse situation. Both qualitative and quantitative methods are applied for data collection. It is found that squatters social security is weak, victimized by severe health problems earning is not regular with lack of physical facilities and overall livelihood is critical. This study helps to understand what the changes that have occurred in livelihood patterns and how poor people survive in urban area.

scholarly journals Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tatsuhiko Hoshino ◽  
Ryohei Nakao ◽  
Hideyuki Doi ◽  
Toshifumi Minamoto
Keyword(s):  
Fish Species ◽  
High Throughput Sequencing ◽  
Correlation Coefficients ◽  
Environmental Dna ◽  
Digital Pcr ◽  
Absolute Quantification ◽  
Taqman Probe ◽  
Individual Species ◽  
Natural Environments ◽  
Target Sequence

AbstractThe combination of high-throughput sequencing technology and environmental DNA (eDNA) analysis has the potential to be a powerful tool for comprehensive, non-invasive monitoring of species in the environment. To understand the correlation between the abundance of eDNA and that of species in natural environments, we have to obtain quantitative eDNA data, usually via individual assays for each species. The recently developed quantitative sequencing (qSeq) technique enables simultaneous phylogenetic identification and quantification of individual species by counting random tags added to the 5′ end of the target sequence during the first DNA synthesis. Here, we applied qSeq to eDNA analysis to test its effectiveness in biodiversity monitoring. eDNA was extracted from water samples taken over 4 days from aquaria containing five fish species (Hemigrammocypris neglectus, Candidia temminckii, Oryzias latipes, Rhinogobius flumineus, and Misgurnus anguillicaudatus), and quantified by qSeq and microfluidic digital PCR (dPCR) using a TaqMan probe. The eDNA abundance quantified by qSeq was consistent with that quantified by dPCR for each fish species at each sampling time. The correlation coefficients between qSeq and dPCR were 0.643, 0.859, and 0.786 for H. neglectus, O. latipes, and M. anguillicaudatus, respectively, indicating that qSeq accurately quantifies fish eDNA.

scholarly journals The Wood Quality of Small-Leaved Lime (Tilia cordata Mill.) Trees in an Urban Area: A Pilot Study

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 420
Author(s):  
Benas Šilinskas ◽  
Aistė Povilaitienė ◽  
Gintautas Urbaitis ◽  
Marius Aleinikovas ◽  
Iveta Varnagirytė-Kabašinskienė
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Wood Quality ◽  
Wood Products ◽  
Urban Trees ◽  
Measuring System ◽  
Forest Site ◽  
Tilia Cordata ◽  
Mean Values ◽  
Wood Development

This study performed a pilot evaluation of the wood quality—defined by a single parameter: dynamic modulus of elasticity (MOEdyn, N mm−2)—of small-leaved lime (Tilia cordata Mill.) trees in urban areas. A search of the literature revealed few studies which examined the specifics of tree wood development in urban areas. Little is known about the potential of wood from urban trees wood of their suitability for the timber industry. In this study, an acoustic velocity measuring system was used for wood quality assessment of small-leaved lime trees. The MOEdyn parameter was evaluated for small-leaved lime trees growing in two urban locations (along the streets, and in an urban park), with an additional sample of forest sites taken as the control. MOEdyn was also assessed for small-leaved lime trees visually assigned to different health classes. The obtained mean values of MOEdyn of 90–120-year old small-leaved lime trees in urban areas ranged between 2492.2 and 2715.8 N mm−2. For younger trees, the values of MOEdyn were lower in the urban areas than in the forest site. Otherwise, the results of the study showed that the small-leaved lime wood samples were of relatively good quality, even if the tree was classified as moderately damaged (which could cause a potential risk to the community). Two alternatives for urban tree management can be envisaged: (1) old trees could be left to grow to maintain the sustainability of an urban area until their natural death, or (2) the wood from selected moderately damaged trees could be used to create wood products, ensuring long-term carbon retention.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Cost-efficient factors in local public spending: Detecting relationships between local environments, population size and urban area category

2021 ◽  
pp. 239980832110038
Author(s):  
Hiroki Baba ◽  
Yasushi Asami
Keyword(s):  
Population Size ◽  
Urban Area ◽  
Urban Areas ◽  
Local Governments ◽  
Statistical Significance ◽  
Population Decline ◽  
Public Spending ◽  
Local Environment ◽  
Environment Factors ◽  
Major Factors

This study examines regional differences in local environment factors to better understand the sustainability of local governments indexed by per capita public spending. Under the condition of heterogeneous population size, we examine how factor characteristics differ depending on the spatial context represented by the urban area category. By employing a Cobb–Douglas cost function with congestion effects on public service provision, the estimated factors enable us to articulate major factors and differences in cost-efficiency between urban area categories. We found that statistical significance and even the signatures of local environment factors differ depending on the urban employment area category. Regarding factors such as the ratios of employees in secondary and tertiary industries, these did not tend to be statistically significant in small-sized urban areas, while small-sized cities in large-sized urban areas were likely to gain confidence intervals. Moreover, we did not observe any statistical significance for the ratio of elderly people due to the balance of spending between national and local governments. These findings could contribute to sustainable management of cities in the advent of population decline.

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